Overview

Dataset statistics

Number of variables13
Number of observations10886
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory104.0 B

Variable types

Numeric8
Categorical5

Warnings

datetime has a high cardinality: 10886 distinct values High cardinality
temp is highly correlated with atempHigh correlation
atemp is highly correlated with tempHigh correlation
casual is highly correlated with countHigh correlation
registered is highly correlated with countHigh correlation
count is highly correlated with casual and 1 other fieldsHigh correlation
temp is highly correlated with atemp and 1 other fieldsHigh correlation
atemp is highly correlated with temp and 1 other fieldsHigh correlation
casual is highly correlated with temp and 3 other fieldsHigh correlation
registered is highly correlated with casual and 1 other fieldsHigh correlation
count is highly correlated with casual and 1 other fieldsHigh correlation
temp is highly correlated with atempHigh correlation
atemp is highly correlated with tempHigh correlation
casual is highly correlated with registered and 1 other fieldsHigh correlation
registered is highly correlated with casual and 1 other fieldsHigh correlation
count is highly correlated with casual and 1 other fieldsHigh correlation
season is highly correlated with atemp and 2 other fieldsHigh correlation
count is highly correlated with casual and 1 other fieldsHigh correlation
casual is highly correlated with count and 3 other fieldsHigh correlation
atemp is highly correlated with season and 3 other fieldsHigh correlation
Unnamed: 0 is highly correlated with season and 2 other fieldsHigh correlation
temp is highly correlated with season and 3 other fieldsHigh correlation
registered is highly correlated with count and 1 other fieldsHigh correlation
Unnamed: 0 is uniformly distributed Uniform
datetime is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
datetime has unique values Unique
windspeed has 1313 (12.1%) zeros Zeros
casual has 986 (9.1%) zeros Zeros

Reproduction

Analysis started2021-07-05 09:03:04.084590
Analysis finished2021-07-05 09:03:11.775064
Duration7.69 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct10886
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5442.5
Minimum0
Maximum10885
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size85.2 KiB
2021-07-05T09:03:11.843390image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile544.25
Q12721.25
median5442.5
Q38163.75
95-th percentile10340.75
Maximum10885
Range10885
Interquartile range (IQR)5442.5

Descriptive statistics

Standard deviation3142.661849
Coefficient of variation (CV)0.5774298299
Kurtosis-1.2
Mean5442.5
Median Absolute Deviation (MAD)2721.5
Skewness0
Sum59247055
Variance9876323.5
MonotonicityStrictly increasing
2021-07-05T09:03:11.957889image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20471
 
< 0.1%
33631
 
< 0.1%
74731
 
< 0.1%
54241
 
< 0.1%
95181
 
< 0.1%
33711
 
< 0.1%
13221
 
< 0.1%
74651
 
< 0.1%
54161
 
< 0.1%
95101
 
< 0.1%
Other values (10876)10876
99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
108851
< 0.1%
108841
< 0.1%
108831
< 0.1%
108821
< 0.1%
108811
< 0.1%
108801
< 0.1%
108791
< 0.1%
108781
< 0.1%
108771
< 0.1%
108761
< 0.1%

datetime
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct10886
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size85.2 KiB
2012-06-15 11:00:00
 
1
2011-05-12 07:00:00
 
1
2011-08-19 13:00:00
 
1
2012-09-02 21:00:00
 
1
2011-04-19 06:00:00
 
1
Other values (10881)
10881 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters206834
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10886 ?
Unique (%)100.0%

Sample

1st row2011-01-01 00:00:00
2nd row2011-01-01 01:00:00
3rd row2011-01-01 02:00:00
4th row2011-01-01 03:00:00
5th row2011-01-01 04:00:00

Common Values

ValueCountFrequency (%)
2012-06-15 11:00:001
 
< 0.1%
2011-05-12 07:00:001
 
< 0.1%
2011-08-19 13:00:001
 
< 0.1%
2012-09-02 21:00:001
 
< 0.1%
2011-04-19 06:00:001
 
< 0.1%
2012-01-02 18:00:001
 
< 0.1%
2012-12-18 14:00:001
 
< 0.1%
2011-03-06 02:00:001
 
< 0.1%
2011-03-17 00:00:001
 
< 0.1%
2011-03-09 19:00:001
 
< 0.1%
Other values (10876)10876
99.9%

Length

2021-07-05T09:03:12.155193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20:00:00456
 
2.1%
17:00:00456
 
2.1%
15:00:00456
 
2.1%
23:00:00456
 
2.1%
12:00:00456
 
2.1%
13:00:00456
 
2.1%
14:00:00456
 
2.1%
18:00:00456
 
2.1%
16:00:00456
 
2.1%
21:00:00456
 
2.1%
Other values (470)17212
79.1%

Most occurring characters

ValueCountFrequency (%)
075100
36.3%
133624
16.3%
222493
 
10.9%
-21772
 
10.5%
:21772
 
10.5%
10886
 
5.3%
33393
 
1.6%
72972
 
1.4%
52969
 
1.4%
62969
 
1.4%
Other values (3)8884
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number152404
73.7%
Dash Punctuation21772
 
10.5%
Other Punctuation21772
 
10.5%
Space Separator10886
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
075100
49.3%
133624
22.1%
222493
 
14.8%
33393
 
2.2%
72972
 
2.0%
52969
 
1.9%
62969
 
1.9%
92969
 
1.9%
82960
 
1.9%
42955
 
1.9%
Dash Punctuation
ValueCountFrequency (%)
-21772
100.0%
Space Separator
ValueCountFrequency (%)
10886
100.0%
Other Punctuation
ValueCountFrequency (%)
:21772
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common206834
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
075100
36.3%
133624
16.3%
222493
 
10.9%
-21772
 
10.5%
:21772
 
10.5%
10886
 
5.3%
33393
 
1.6%
72972
 
1.4%
52969
 
1.4%
62969
 
1.4%
Other values (3)8884
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII206834
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
075100
36.3%
133624
16.3%
222493
 
10.9%
-21772
 
10.5%
:21772
 
10.5%
10886
 
5.3%
33393
 
1.6%
72972
 
1.4%
52969
 
1.4%
62969
 
1.4%
Other values (3)8884
 
4.3%

season
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size85.2 KiB
4
2734 
2
2733 
3
2733 
1
2686 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10886
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
42734
25.1%
22733
25.1%
32733
25.1%
12686
24.7%

Length

2021-07-05T09:03:12.334430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-05T09:03:12.393391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
42734
25.1%
32733
25.1%
22733
25.1%
12686
24.7%

Most occurring characters

ValueCountFrequency (%)
42734
25.1%
22733
25.1%
32733
25.1%
12686
24.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10886
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
42734
25.1%
22733
25.1%
32733
25.1%
12686
24.7%

Most occurring scripts

ValueCountFrequency (%)
Common10886
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
42734
25.1%
22733
25.1%
32733
25.1%
12686
24.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII10886
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
42734
25.1%
22733
25.1%
32733
25.1%
12686
24.7%

holiday
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size85.2 KiB
0
10575 
1
 
311

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10886
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010575
97.1%
1311
 
2.9%

Length

2021-07-05T09:03:12.535318image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-05T09:03:12.582178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
010575
97.1%
1311
 
2.9%

Most occurring characters

ValueCountFrequency (%)
010575
97.1%
1311
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10886
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010575
97.1%
1311
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common10886
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010575
97.1%
1311
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII10886
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010575
97.1%
1311
 
2.9%

workingday
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size85.2 KiB
1
7412 
0
3474 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10886
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
17412
68.1%
03474
31.9%

Length

2021-07-05T09:03:12.804984image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-05T09:03:12.850968image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
17412
68.1%
03474
31.9%

Most occurring characters

ValueCountFrequency (%)
17412
68.1%
03474
31.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10886
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
17412
68.1%
03474
31.9%

Most occurring scripts

ValueCountFrequency (%)
Common10886
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
17412
68.1%
03474
31.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII10886
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17412
68.1%
03474
31.9%

weather
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size85.2 KiB
1
7192 
2
2834 
3
859 
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10886
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
17192
66.1%
22834
 
26.0%
3859
 
7.9%
41
 
< 0.1%

Length

2021-07-05T09:03:12.984072image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-05T09:03:13.033960image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
17192
66.1%
22834
 
26.0%
3859
 
7.9%
41
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
17192
66.1%
22834
 
26.0%
3859
 
7.9%
41
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10886
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
17192
66.1%
22834
 
26.0%
3859
 
7.9%
41
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common10886
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
17192
66.1%
22834
 
26.0%
3859
 
7.9%
41
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10886
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17192
66.1%
22834
 
26.0%
3859
 
7.9%
41
 
< 0.1%

temp
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct49
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.23085982
Minimum0.82
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.2 KiB
2021-07-05T09:03:13.103952image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.82
5-th percentile8.2
Q113.94
median20.5
Q326.24
95-th percentile32.8
Maximum41
Range40.18
Interquartile range (IQR)12.3

Descriptive statistics

Standard deviation7.791589844
Coefficient of variation (CV)0.3851338951
Kurtosis-0.9145302638
Mean20.23085982
Median Absolute Deviation (MAD)6.56
Skewness0.003690844422
Sum220233.14
Variance60.7088723
MonotonicityNot monotonic
2021-07-05T09:03:13.199610image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
14.76467
 
4.3%
26.24453
 
4.2%
28.7427
 
3.9%
13.94413
 
3.8%
18.86406
 
3.7%
22.14403
 
3.7%
25.42403
 
3.7%
16.4400
 
3.7%
22.96395
 
3.6%
27.06394
 
3.6%
Other values (39)6725
61.8%
ValueCountFrequency (%)
0.827
 
0.1%
1.642
 
< 0.1%
2.465
 
< 0.1%
3.2811
 
0.1%
4.144
 
0.4%
4.9260
 
0.6%
5.74107
1.0%
6.56146
1.3%
7.38106
1.0%
8.2229
2.1%
ValueCountFrequency (%)
411
 
< 0.1%
39.366
 
0.1%
38.547
 
0.1%
37.7234
 
0.3%
36.946
 
0.4%
36.0823
 
0.2%
35.2676
 
0.7%
34.4480
 
0.7%
33.62130
1.2%
32.8202
1.9%

atemp
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct60
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.65508405
Minimum0.76
Maximum45.455
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.2 KiB
2021-07-05T09:03:13.303109image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.76
5-th percentile9.85
Q116.665
median24.24
Q331.06
95-th percentile36.365
Maximum45.455
Range44.695
Interquartile range (IQR)14.395

Descriptive statistics

Standard deviation8.474600626
Coefficient of variation (CV)0.3582570498
Kurtosis-0.8500756472
Mean23.65508405
Median Absolute Deviation (MAD)6.82
Skewness-0.1025595135
Sum257509.245
Variance71.81885578
MonotonicityNot monotonic
2021-07-05T09:03:13.414613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.06671
 
6.2%
25.76423
 
3.9%
22.725406
 
3.7%
20.455400
 
3.7%
26.515395
 
3.6%
16.665381
 
3.5%
25365
 
3.4%
33.335364
 
3.3%
21.21356
 
3.3%
30.305350
 
3.2%
Other values (50)6775
62.2%
ValueCountFrequency (%)
0.762
 
< 0.1%
1.5151
 
< 0.1%
2.2757
 
0.1%
3.037
 
0.1%
3.7916
 
0.1%
4.54511
 
0.1%
5.30525
 
0.2%
6.0673
0.7%
6.8263
0.6%
7.57575
0.7%
ValueCountFrequency (%)
45.4551
 
< 0.1%
44.6953
 
< 0.1%
43.947
 
0.1%
43.187
 
0.1%
42.42524
 
0.2%
41.66523
 
0.2%
40.9139
0.4%
40.1545
0.4%
39.39567
0.6%
38.63574
0.7%

humidity
Real number (ℝ≥0)

Distinct89
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.88645967
Minimum0
Maximum100
Zeros22
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size85.2 KiB
2021-07-05T09:03:13.536224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile31
Q147
median62
Q377
95-th percentile93
Maximum100
Range100
Interquartile range (IQR)30

Descriptive statistics

Standard deviation19.24503328
Coefficient of variation (CV)0.3109732465
Kurtosis-0.7598175375
Mean61.88645967
Median Absolute Deviation (MAD)15
Skewness-0.08633518365
Sum673696
Variance370.3713058
MonotonicityNot monotonic
2021-07-05T09:03:13.634567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88368
 
3.4%
94324
 
3.0%
83316
 
2.9%
87289
 
2.7%
70259
 
2.4%
65253
 
2.3%
46247
 
2.3%
66246
 
2.3%
77244
 
2.2%
49234
 
2.1%
Other values (79)8106
74.5%
ValueCountFrequency (%)
022
0.2%
81
 
< 0.1%
101
 
< 0.1%
121
 
< 0.1%
131
 
< 0.1%
142
 
< 0.1%
154
 
< 0.1%
168
 
0.1%
176
 
0.1%
187
 
0.1%
ValueCountFrequency (%)
100148
1.4%
971
 
< 0.1%
961
 
< 0.1%
94324
3.0%
93205
1.9%
922
 
< 0.1%
911
 
< 0.1%
904
 
< 0.1%
89150
1.4%
88368
3.4%

windspeed
Real number (ℝ≥0)

ZEROS

Distinct28
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.79939541
Minimum0
Maximum56.9969
Zeros1313
Zeros (%)12.1%
Negative0
Negative (%)0.0%
Memory size85.2 KiB
2021-07-05T09:03:13.719554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17.0015
median12.998
Q316.9979
95-th percentile27.9993
Maximum56.9969
Range56.9969
Interquartile range (IQR)9.9964

Descriptive statistics

Standard deviation8.164537327
Coefficient of variation (CV)0.6378846084
Kurtosis0.6301328693
Mean12.79939541
Median Absolute Deviation (MAD)5.9965
Skewness0.5887665266
Sum139334.2184
Variance66.65966976
MonotonicityNot monotonic
2021-07-05T09:03:13.794897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
01313
12.1%
8.99811120
10.3%
11.00141057
9.7%
12.9981042
9.6%
7.00151034
9.5%
15.0013961
8.8%
6.0032872
8.0%
16.9979824
7.6%
19.0012676
6.2%
19.9995492
 
4.5%
Other values (18)1495
13.7%
ValueCountFrequency (%)
01313
12.1%
6.0032872
8.0%
7.00151034
9.5%
8.99811120
10.3%
11.00141057
9.7%
12.9981042
9.6%
15.0013961
8.8%
16.9979824
7.6%
19.0012676
6.2%
19.9995492
 
4.5%
ValueCountFrequency (%)
56.99692
 
< 0.1%
51.99871
 
< 0.1%
50.00211
 
< 0.1%
47.99882
 
< 0.1%
46.00223
 
< 0.1%
43.99898
 
0.1%
43.000612
0.1%
40.997311
0.1%
39.000727
0.2%
36.997422
0.2%

casual
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct309
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.0219548
Minimum0
Maximum367
Zeros986
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size85.2 KiB
2021-07-05T09:03:13.884564image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median17
Q349
95-th percentile141
Maximum367
Range367
Interquartile range (IQR)45

Descriptive statistics

Standard deviation49.96047657
Coefficient of variation (CV)1.38694518
Kurtosis7.551629306
Mean36.0219548
Median Absolute Deviation (MAD)15
Skewness2.495748398
Sum392135
Variance2496.049219
MonotonicityNot monotonic
2021-07-05T09:03:13.981209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0986
 
9.1%
1667
 
6.1%
2487
 
4.5%
3438
 
4.0%
4354
 
3.3%
5332
 
3.0%
6269
 
2.5%
7250
 
2.3%
8250
 
2.3%
9230
 
2.1%
Other values (299)6623
60.8%
ValueCountFrequency (%)
0986
9.1%
1667
6.1%
2487
4.5%
3438
4.0%
4354
 
3.3%
5332
 
3.0%
6269
 
2.5%
7250
 
2.3%
8250
 
2.3%
9230
 
2.1%
ValueCountFrequency (%)
3671
< 0.1%
3621
< 0.1%
3611
< 0.1%
3571
< 0.1%
3561
< 0.1%
3551
< 0.1%
3541
< 0.1%
3521
< 0.1%
3501
< 0.1%
3321
< 0.1%

registered
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct731
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.5521771
Minimum0
Maximum886
Zeros15
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size85.2 KiB
2021-07-05T09:03:14.084637image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q136
median118
Q3222
95-th percentile464
Maximum886
Range886
Interquartile range (IQR)186

Descriptive statistics

Standard deviation151.0390331
Coefficient of variation (CV)0.9709863011
Kurtosis2.626081
Mean155.5521771
Median Absolute Deviation (MAD)90
Skewness1.524804587
Sum1693341
Variance22812.78951
MonotonicityNot monotonic
2021-07-05T09:03:14.178184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3195
 
1.8%
4190
 
1.7%
5177
 
1.6%
6155
 
1.4%
2150
 
1.4%
1135
 
1.2%
7126
 
1.2%
8114
 
1.0%
9114
 
1.0%
1187
 
0.8%
Other values (721)9443
86.7%
ValueCountFrequency (%)
015
 
0.1%
1135
1.2%
2150
1.4%
3195
1.8%
4190
1.7%
5177
1.6%
6155
1.4%
7126
1.2%
8114
1.0%
9114
1.0%
ValueCountFrequency (%)
8861
< 0.1%
8572
< 0.1%
8391
< 0.1%
8331
< 0.1%
8122
< 0.1%
8111
< 0.1%
8071
< 0.1%
8061
< 0.1%
8031
< 0.1%
8021
< 0.1%

count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct822
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean191.5741319
Minimum1
Maximum977
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.2 KiB
2021-07-05T09:03:14.276100image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q142
median145
Q3284
95-th percentile563.75
Maximum977
Range976
Interquartile range (IQR)242

Descriptive statistics

Standard deviation181.1444538
Coefficient of variation (CV)0.9455580042
Kurtosis1.300092952
Mean191.5741319
Median Absolute Deviation (MAD)114
Skewness1.242066212
Sum2085476
Variance32813.31315
MonotonicityNot monotonic
2021-07-05T09:03:14.379838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5169
 
1.6%
4149
 
1.4%
3144
 
1.3%
6135
 
1.2%
2132
 
1.2%
7118
 
1.1%
1105
 
1.0%
899
 
0.9%
1095
 
0.9%
1195
 
0.9%
Other values (812)9645
88.6%
ValueCountFrequency (%)
1105
1.0%
2132
1.2%
3144
1.3%
4149
1.4%
5169
1.6%
6135
1.2%
7118
1.1%
899
0.9%
983
0.8%
1095
0.9%
ValueCountFrequency (%)
9771
< 0.1%
9701
< 0.1%
9681
< 0.1%
9481
< 0.1%
9431
< 0.1%
9251
< 0.1%
9171
< 0.1%
9011
< 0.1%
9001
< 0.1%
8971
< 0.1%

Interactions

2021-07-05T09:03:06.101236image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:06.191540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:06.276330image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:06.358558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:06.435408image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:06.514031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:06.594864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:06.669748image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:06.746586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:06.831629image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:06.921850image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:07.004559image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:07.164463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:07.266201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:07.354847image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:07.437783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:07.523837image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:07.597927image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:07.676533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:07.748147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:07.821759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:07.895578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:07.973297image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:08.045483image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:08.118613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:08.195563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:08.277699image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:08.356386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:08.436606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:08.536319image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:08.617437image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:08.692079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:08.768757image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:08.846212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:08.928114image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:09.003361image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:09.079828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:09.156243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:09.239194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:09.315322image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:09.392798image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:09.476038image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:09.563769image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:09.653019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:09.826474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:09.909036image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:09.995619image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:10.075995image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:10.157876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:10.233796image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:10.313653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:10.385130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:10.458796image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:10.533906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:10.611535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:10.683244image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:10.757025image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:10.836347image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:10.925338image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:11.019307image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:11.100088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:11.176372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:11.258620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-05T09:03:11.333164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-07-05T09:03:14.477100image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-07-05T09:03:14.605370image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-07-05T09:03:14.753294image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-07-05T09:03:14.879947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-07-05T09:03:14.987611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-07-05T09:03:11.481592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-07-05T09:03:11.699624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Unnamed: 0datetimeseasonholidayworkingdayweathertempatemphumiditywindspeedcasualregisteredcount
002011-01-01 00:00:0010019.8414.395810.000031316
112011-01-01 01:00:0010019.0213.635800.000083240
222011-01-01 02:00:0010019.0213.635800.000052732
332011-01-01 03:00:0010019.8414.395750.000031013
442011-01-01 04:00:0010019.8414.395750.0000011
552011-01-01 05:00:0010029.8412.880756.0032011
662011-01-01 06:00:0010019.0213.635800.0000202
772011-01-01 07:00:0010018.2012.880860.0000123
882011-01-01 08:00:0010019.8414.395750.0000178
992011-01-01 09:00:00100113.1217.425760.00008614

Last rows

Unnamed: 0datetimeseasonholidayworkingdayweathertempatemphumiditywindspeedcasualregisteredcount
10876108762012-12-19 14:00:00401117.2221.2105012.998033185218
10877108772012-12-19 15:00:00401117.2221.2105019.001228209237
10878108782012-12-19 16:00:00401117.2221.2105023.999437297334
10879108792012-12-19 17:00:00401116.4020.4555026.002726536562
10880108802012-12-19 18:00:00401115.5819.6955023.999423546569
10881108812012-12-19 19:00:00401115.5819.6955026.00277329336
10882108822012-12-19 20:00:00401114.7617.4255715.001310231241
10883108832012-12-19 21:00:00401113.9415.9106115.00134164168
10884108842012-12-19 22:00:00401113.9417.425616.003212117129
10885108852012-12-19 23:00:00401113.1216.665668.998148488